wehub-resource-sync
118 lines
3.0 KiB
Markdown
118 lines
3.0 KiB
Markdown
# Model Context Protocol (MCP) Python Implementation
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This repository contains a Python implementation of the Model Context Protocol (MCP), demonstrating how to create both a server and client application that communicate using the MCP standard.
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## Overview
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The MCP implementation consists of two main components:
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1. **MCP Server (`server.py`)** - A server that exposes:
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- **Tools**: Functions that can be called remotely
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- **Resources**: Data that can be retrieved
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- **Prompts**: Templates for generating prompts for language models
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2. **MCP Client (`client.py`)** - A client application that connects to the server and uses its features
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## Features
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This implementation demonstrates several key MCP features:
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### Tools
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- `completion` - Generates text completions from AI models (simulated)
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- `add` - Simple calculator that adds two numbers
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### Resources
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- `models://` - Returns information about available AI models
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- `greeting://{name}` - Returns a personalized greeting for a given name
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### Prompts
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- `review_code` - Generates a prompt for reviewing code
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## Installation
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To use this MCP implementation, install the required packages:
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```powershell
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pip install mcp-server mcp-client
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```
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## Running the Server and Client
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### Starting the Server
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Run the server in one terminal window:
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```powershell
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python server.py
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```
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The server can also be run in development mode using the MCP CLI:
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```powershell
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mcp dev server.py
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```
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Or installed in Claude Desktop (if available):
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```powershell
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mcp install server.py
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```
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### Running the Client
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Run the client in another terminal window:
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```powershell
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python client.py
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```
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This will connect to the server and demonstrate all available features.
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### Client Usage
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The client (`client.py`) demonstrates all the MCP capabilities:
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```powershell
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python client.py
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```
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This will connect to the server and exercise all features including tools, resources, and prompts. The output will show:
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1. Calculator tool result (5 + 7 = 12)
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2. Completion tool response to "What is the meaning of life?"
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3. List of available AI models
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4. Personalized greeting for "MCP Explorer"
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5. Code review prompt template
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## Implementation Details
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The server is implemented using the `FastMCP` API, which provides high-level abstractions for defining MCP services. Here's a simplified example of how tools are defined:
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```python
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@mcp.tool()
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def add(a: int, b: int) -> int:
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"""Add two numbers together
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Args:
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a: First number
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b: Second number
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Returns:
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The sum of the two numbers
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"""
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logger.info(f"Adding {a} and {b}")
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return a + b
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```
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The client uses the MCP client library to connect to and call the server:
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```python
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async with stdio_client(server_params) as (reader, writer):
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async with ClientSession(reader, writer) as session:
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await session.initialize()
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result = await session.call_tool("add", arguments={"a": 5, "b": 7})
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```
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## Learn More
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For more information about MCP, visit: https://modelcontextprotocol.io/ |